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Housing conditions in Tunisia: the quantity-quality mismatch

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Abstract

The aim of this paper is to analyze the housing conditions of households in Tunisia, for which we develop an individual indicator of housing adequacy based on dwelling attributes and household size. This indicator has two main advantages: it relies on less arbitrary weights, and it takes the multidimensional housing facets into account in the aggregation process. Using data from Tunisia’s national survey on family health and social transformations for two periods (1994 and 2006), our findings are comparable to those from an arbitrary weighting scheme, the most commonly adopted in the literature of multidimensional well-being. In order to identify households suffering from housing deprivation, we develop five housing deprivation thresholds related to some public health issues. Our results suggest that quality-based efforts should be made to reduce the gap in housing conditions between coastal and inland regions and between urban and rural areas. These efforts should be aimed at reducing housing deprivation and preventing health vulnerability among deprived households.

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Notes

  1. See Angel et al. (1993), Malpezzi and Mayo (1997), PNUD (2004), table B3–B4 in UNHSP (2003), Malpezzi and Sa-Adu (1996) for African countries and Gordon et al.'s (2007) final report on the extent and nature of absolute poverty.

  2. These dimensions are: Legal security of tenure; Availability of services, materials, facilities and infrastructure; Affordability; Habitability; Accessibility; Location; and Cultural adequacy. See Article 11(1) of the Covenant for more details.

  3. The main criticism addressed at HDI relates to the arbitrary weights used and the correlation between its components.

  4. See, for example, Follain and Jimenez (1985) and Sheppard (1999) for an analysis of the hedonic markets, Arnott (1987) for a theoretical survey and Whitehead (1999) for a survey of the urban markets.

  5. MCA is a generalization of Correspondence Analysis (CA), which is intended to discern the pattern of the relationship of several categorical variables.

  6. See Greenacre (1993) and Asselin (2002) for a review of multiple correspondence analysis.

  7. All housing attributes that characterize the housing stock are implicitly exchanged in the market.

  8. See statistical appendix in Baharoglu et al. (2005).

  9. Own calculations from different INS publications show that, after a peak in the mid-1980s (6%), housing investment became nearly steady after the early 1990s, representing around 4% of the Gross Domestic Product.

  10. Key indicators of slum dwelling are: insecure residential status, inadequate access to safe water, inadequate access to sanitation and other infrastructure, poor structural quality of housing and overcrowding. See UNHSP (2003).

  11. Decree 74, Article 2 on the mission and functions of the Tunisian Department of Public Health.

  12. Shares are more important if we keep in mind that the inertia of the first factor is severely underestimated due to the existence of additional artificial dimensions (with eigenvalues less than 1/K) generated by the coding scheme of categorical variables in MCA. See Greenacre (1993).

  13. The first-order administrative division, also known as wilaya.

  14. The geographic area formed by the capital Tunis and three contiguous governorates Ariana, Ben Arous and Manouba. We note that Manouba became a first-administrative division after 1994.

  15. In 2000, the incidence of poverty was 8.3% in rural areas and 1.6% among urban households, giving a national rate of 4.1%. See PNUD (2004) p: 86.

  16. One argument for this choice is that a deficient housing attribute may be compensated by others in the HCI.

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Acknowledgments

The author would like to thank the anonymous referees for their valuable comments and suggestions.

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Authors and Affiliations

  1. Department of Management and Quantitative Methods, Faculté des Sciences Economiques et de Gestion de Sousse, Cité Erriadh, 4023, Sousse, Tunisia

    Radhouane Filali

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Correspondence to Radhouane Filali.

Appendix

Appendix

See Figs. 4, 5, 6 and Table 6.

Fig. 4
figure 4

MCA coordinates plot on the first factor plane

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Fig. 5
figure 5

HCI (%) by housing dimension and region

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Fig. 6
figure 6

HCI cumulative distributive function

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Table 6 Multiple correspondence analysis
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Filali, R. Housing conditions in Tunisia: the quantity-quality mismatch. J Hous and the Built Environ 27, 317–347 (2012). https://doi.org/10.1007/s10901-012-9271-z

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